Network Cost-Aware Geo-Distributed Data Analytics System

Kwangsung Oh, Minmin Zhang, Abhishek Chandra, Jon Weissman

Research output: Contribution to journalArticlepeer-review


Many geo-distributed data analytics (GDA) systems have focused on the network performance-bottleneck: inter-data center network bandwidth to improve performance. Unfortunately, these systems may encounter a cost-bottleneck (${\$}$$) because they have not considered data transfer cost (${\$}$$), one of the most expensive and heterogeneous resources in a multi-cloud environment. In this article, we present Kimchi, a network cost-aware GDA system to meet the cost-performance tradeoff by exploiting data transfer cost heterogeneity to avoid the cost-bottleneck. Kimchi determines cost-aware task placement decisions for scheduling tasks given inputs including data transfer cost, network bandwidth, input data size and locations, and desired cost-performance tradeoff preference. In addition, Kimchi is also mindful of data transfer cost in the presence of dynamics. Kimchi has been applied to two common GDA MapReduce models: synchronous barrier and asynchronous push-based shuffle. A Kimchi prototype has been implemented on Spark, and experiments show that it reduces cost by 5% $\scriptstyle \sim$∼ 24% without impacting performance and reduces query execution time by 45% $\scriptstyle \sim$∼ 70% without impacting cost compared to other baseline approaches centralized, vanilla Spark, and bandwidth-aware (e.g., Iridium). More importantly, Kimchi allows applications to explore a much richer cost-performance tradeoff space in a multi-cloud environment.

Original languageEnglish (US)
Pages (from-to)1407-1420
Number of pages14
JournalIEEE Transactions on Parallel and Distributed Systems
Issue number6
StatePublished - Jun 1 2022

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  • Geo-distributed data
  • data analytics system
  • multi cloud providers
  • multi-DCs


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